1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
22/3/2025 Comida 76766 Tami Supermercado
21/3/2025 Diosi 18500 Andrés antiparasitario
27/3/2025 Gas 82450 Andrés NA
26/3/2025 Comida 4000 Andrés avena multigrano y chucrut
29/3/2025 Comida 70591 Tami Supermercado
3/4/2025 Gas 83300 Andrés NA
4/4/2025 Agua 20807 Andrés NA
6/4/2025 Comida 52655 Tami Supermercado
12/4/2025 Comida 72108 Tami Supermercado
16/4/2025 VTR 21990 Andrés NA
22/4/2025 Comida 107881 Tami Supermercado
26/4/2025 Comida 55874 Tami Supermercado
28/4/2025 Comida 13050 Tami Cervezas MUT
29/4/2025 Electricidad 52507 Andrés enel
29/4/2025 Diosi 11990 Andrés arena 7kg superzoo
3/5/2025 Agua 17072 Andrés aguas andina
13/5/2025 VTR 22000 Andrés NA
17/5/2025 Electricidad 52404 Andrés NA
13/6/2025 VTR 22000 Andrés NA
22/6/2025 Electricidad 52401 Andrés NA
27/7/2025 Electricidad 52000 Andrés NA
27/7/2025 Comida 59147 Tami Supermercado
29/7/2025 Comida 10000 Andrés complemento comida
29/7/2025 Electrodomésticos/mantención casa 68000 Andrés NA
30/7/2025 Comida 24140 Tami Supermercado
31/7/2025 Gas 19100 Andrés NA
3/8/2025 Comida 86089 Tami Supermercado
10/8/2025 Comida 108649 Tami Supermercado
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))

tsData_gastos = decompose(tsData_gastos)

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7  # Weekly data
num_periods <- length(tsData_gastos$x)  # Total number of periods in your time series

# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)

# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
  day = dates,
  Actual = as.numeric(tsData_gastos$x),
  Seasonal = as.numeric(tsData_gastos$seasonal),
  Trend = as.numeric(tsData_gastos$trend),
  Random = as.numeric(tsData_gastos$random)
)

tsData_gastos_long <- tsData_gastos_df %>%
  pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"), 
               names_to = "Component", values_to = "Value")

# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
  facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme(strip.text = element_text(size = 12))

#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df  F value Pr(>F)    
## interrupt_var 1.0208e+09   2    5.262 0.0054 ** 
## lag_depvar    2.6341e+11   1 2715.568 <2e-16 ***
## Residuals     8.1964e+10 845                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838 -1785.785 16243.46 0.1442934
## 2-0 31323.581 23214.473 39432.69 0.0000000
## 2-1 24094.743 19404.285 28785.20 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
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## 29   28706.00             0   28640.00
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## 710 114267.57             2  115594.71
## 711  88353.29             2  114267.57
## 712  88750.86             2   88353.29
## 713  78835.71             2   88750.86
## 714  75519.14             2   78835.71
## 715  73202.86             2   75519.14
## 716  53433.29             2   73202.86
## 717  48165.71             2   53433.29
## 718  52163.14             2   48165.71
## 719  49306.86             2   52163.14
## 720  36846.86             2   49306.86
## 721  43220.57             2   36846.86
## 722  38952.29             2   43220.57
## 723  41522.29             2   38952.29
## 724  39090.00             2   41522.29
## 725  28452.57             2   39090.00
## 726  32975.00             2   28452.57
## 727  33690.71             2   32975.00
## 728  26405.29             2   33690.71
## 729  47087.43             2   26405.29
## 730  49660.29             2   47087.43
## 731  47409.71             2   49660.29
## 732  53881.71             2   47409.71
## 733  45189.57             2   53881.71
## 734  45503.86             2   45189.57
## 735  54640.14             2   45503.86
## 736  39131.29             2   54640.14
## 737  35024.14             2   39131.29
## 738  44755.43             2   35024.14
## 739  41063.29             2   44755.43
## 740  42783.29             2   41063.29
## 741  45952.57             2   42783.29
## 742  44937.43             2   45952.57
## 743  40838.43             2   44937.43
## 744  48838.43             2   40838.43
## 745  43139.14             2   48838.43
## 746  67134.29             2   43139.14
## 747  73224.29             2   67134.29
## 748  68770.71             2   73224.29
## 749  59539.29             2   68770.71
## 750  82179.86             2   59539.29
## 751  74252.14             2   82179.86
## 752  73015.00             2   74252.14
## 753  56116.43             2   73015.00
## 754 111885.00             2   56116.43
## 755 131425.14             2  111885.00
## 756 136678.00             2  131425.14
## 757 115531.29             2  136678.00
## 758 118310.86             2  115531.29
## 759 117449.43             2  118310.86
## 760 115193.57             2  117449.43
## 761  61025.43             2  115193.57
## 762  43913.86             2   61025.43
## 763  46099.29             2   43913.86
## 764  44524.86             2   46099.29
## 765  42208.71             2   44524.86
## 766 166486.57             2   42208.71
## 767 171565.29             2  166486.57
## 768 200415.71             2  171565.29
## 769 204498.14             2  200415.71
## 770 197558.86             2  204498.14
## 771 195266.57             2  197558.86
## 772 203144.29             2  195266.57
## 773  85493.71             2  203144.29
## 774  74721.57             2   85493.71
## 775  36232.14             2   74721.57
## 776  40161.71             2   36232.14
## 777  40629.86             2   40161.71
## 778  45663.71             2   40629.86
## 779  39252.29             2   45663.71
## 780  39618.57             2   39252.29
## 781  39438.43             2   39618.57
## 782  44650.71             2   39438.43
## 783  38626.71             2   44650.71
## 784  38280.43             2   38626.71
## 785  44134.14             2   38280.43
## 786  47596.43             2   44134.14
## 787  45598.43             2   47596.43
## 788  42564.29             2   45598.43
## 789  45699.14             2   42564.29
## 790  49553.86             2   45699.14
## 791  50018.43             2   49553.86
## 792  43772.86             2   50018.43
## 793  39235.43             2   43772.86
## 794  39905.00             2   39235.43
## 795  40374.43             2   39905.00
## 796  34230.57             2   40374.43
## 797  34324.14             2   34230.57
## 798  33491.57             2   34324.14
## 799  33366.43             2   33491.57
## 800  46646.86             2   33366.43
## 801  49770.86             2   46646.86
## 802  57339.86             2   49770.86
## 803  59799.14             2   57339.86
## 804  53577.14             2   59799.14
## 805  61775.29             2   53577.14
## 806  70627.86             2   61775.29
## 807  57888.43             2   70627.86
## 808  49960.71             2   57888.43
## 809  42923.71             2   49960.71
## 810  47284.86             2   42923.71
## 811  52284.86             2   47284.86
## 812  50191.00             2   52284.86
## 813  36465.86             2   50191.00
## 814  34525.14             2   36465.86
## 815  43199.14             2   34525.14
## 816  52757.43             2   43199.14
## 817  43200.86             2   52757.43
## 818  36772.29             2   43200.86
## 819  29568.00             2   36772.29
## 820  42362.00             2   29568.00
## 821  42566.29             2   42362.00
## 822  39596.00             2   42566.29
## 823  32925.00             2   39596.00
## 824  43416.57             2   32925.00
## 825  52624.86             2   43416.57
## 826  57733.71             2   52624.86
## 827  54120.57             2   57733.71
## 828  53353.43             2   54120.57
## 829  56286.86             2   53353.43
## 830  60626.86             2   56286.86
## 831  61375.29             2   60626.86
## 832  53710.86             2   61375.29
## 833  55795.57             2   53710.86
## 834  55130.14             2   55795.57
## 835  57700.14             2   55130.14
## 836  61333.14             2   57700.14
## 837  59230.71             2   61333.14
## 838  49195.00             2   59230.71
## 839  55436.43             2   49195.00
## 840  50353.14             2   55436.43
## 841  43194.86             2   50353.14
## 842  47539.71             2   43194.86
## 843  35271.00             2   47539.71
## 844  34774.86             2   35271.00
## 845  48788.71             2   34774.86
## 846  50717.71             2   48788.71
## 847  51727.43             2   50717.71
## 848  51313.14             2   51727.43
## 849  56125.29             2   51313.14
## 850  68503.71             2   56125.29
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   693 53557.84 21967.297
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00  60863.86  56293.86  52725.00  58625.00
## [694]  47513.00  40300.14  33312.43  29556.71  27816.71  34120.29  32132.57
## [701]  32902.57  39694.14  72501.29  79551.14  99637.71  95424.29  98395.14
## [708] 115594.71 114267.57  88353.29  88750.86  78835.71  75519.14  73202.86
## [715]  53433.29  48165.71  52163.14  49306.86  36846.86  43220.57  38952.29
## [722]  41522.29  39090.00  28452.57  32975.00  33690.71  26405.29  47087.43
## [729]  49660.29  47409.71  53881.71  45189.57  45503.86  54640.14  39131.29
## [736]  35024.14  44755.43  41063.29  42783.29  45952.57  44937.43  40838.43
## [743]  48838.43  43139.14  67134.29  73224.29  68770.71  59539.29  82179.86
## [750]  74252.14  73015.00  56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57  61025.43  43913.86  46099.29  44524.86
## [764]  42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29  85493.71  74721.57  36232.14  40161.71  40629.86  45663.71
## [778]  39252.29  39618.57  39438.43  44650.71  38626.71  38280.43  44134.14
## [785]  47596.43  45598.43  42564.29  45699.14  49553.86  50018.43  43772.86
## [792]  39235.43  39905.00  40374.43  34230.57  34324.14  33491.57  33366.43
## [799]  46646.86  49770.86  57339.86  59799.14  53577.14  61775.29  70627.86
## [806]  57888.43  49960.71  42923.71  47284.86  52284.86  50191.00  36465.86
## [813]  34525.14  43199.14  52757.43  43200.86  36772.29  29568.00  42362.00
## [820]  42566.29  39596.00  32925.00  43416.57  52624.86  57733.71  54120.57
## [827]  53353.43  56286.86  60626.86  61375.29  53710.86  55795.57  55130.14
## [834]  57700.14  61333.14  59230.71  49195.00  55436.43  50353.14  43194.86
## [841]  47539.71  35271.00  34774.86  48788.71  50717.71  51727.43  51313.14
## [848]  56125.29  68503.71
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   2017.44912   4039.75617   -537.42567   2438.63177  -2968.35392    519.21050 
##            8            9           10           11           12           13 
##  -5655.16546  -1188.46860  -3966.86414   -419.40885  -4940.93886  -1611.49963 
##           14           15           16           17           18           19 
##   -901.81220    375.62363  -3244.23198   -379.68961  -2131.58388   6602.50451 
##           20           21           22           23           24           25 
##  -1529.07823  -1208.44416   1475.31107  -1186.41958    234.64776   1695.18959 
##           26           27           28           29           30           31 
##  -7101.34233    946.75517   8192.17811    420.22636    -11.86174  -2398.46429 
##           32           33           34           35           36           37 
##   1577.74438   4574.48078   1129.99337   2394.68298  -1864.32005   4610.98791 
##           38           39           40           41           42           43 
##   4305.56246  -2272.77845  -2979.36227  -1108.99159 -10740.67465   7287.54286 
##           44           45           46           47           48           49 
##   2557.35902   1367.53946   8106.10577    688.88961   6531.34652   6718.21594 
##           50           51           52           53           54           55 
##  -5877.38656  -4792.43420  -5058.17160  -7928.33905   6128.20684  -4076.58367 
##           56           57           58           59           60           61 
##  -4895.23501   3853.88380    887.03349    -32.61504    141.77959  -4996.78847 
##           62           63           64           65           66           67 
##  18125.32153   3643.58850  -3642.66084   5927.61133   7347.48066  14643.72995 
##           68           69           70           71           72           73 
##   1701.00602 -13205.32503  -1302.49907   4646.55144  -4896.44505  -4402.13919 
##           74           75           76           77           78           79 
## -10496.16833   2465.97578  -5399.75050   1062.85618  -6866.65611    545.35189 
##           80           81           82           83           84           85 
##  -2355.66410  -2695.12783  -3933.34993   -539.41585   2312.99992   3761.80449 
##           86           87           88           89           90           91 
##    476.87957   -484.49571    196.54346   4301.90408  -1162.21219   1151.32773 
##           92           93           94           95           96           97 
##  -2063.81838  -1044.10564    177.56730    274.82356  -7483.93130   2390.54410 
##           98           99          100          101          102          103 
##  -8603.01046  -2942.37550  -4041.27759  -1738.70919  -1263.06025   3179.50270 
##          104          105          106          107          108          109 
##  -2342.62177   2593.22580  -1158.56822    970.19149   2587.08949  -3153.89634 
##          110          111          112          113          114          115 
##  -4723.10797   -851.02809   1902.81481  11693.34230  -1240.69861   2670.01921 
##          116          117          118          119          120          121 
##   4264.68827   3505.14317  -1097.05551  -4713.87922  -3722.64884   2320.52086 
##          122          123          124          125          126          127 
##  -1731.46081   1341.28655   8859.35491    849.74459    133.02283  -2518.87835 
##          128          129          130          131          132          133 
##   2656.82497   7054.63784   1015.64380  -8496.32376   1750.47728   4137.01554 
##          134          135          136          137          138          139 
##  -3161.83629  -1418.36149   -852.94572  -3879.16722   1183.19800   -495.04680 
##          140          141          142          143          144          145 
##  -2913.22972   1718.06824  -1880.79079  -7829.30593   2038.17254  -3480.42918 
##          146          147          148          149          150          151 
##   2101.03552   -258.15001   1022.39376   -359.68207   1351.80007   1186.52074 
##          152          153          154          155          156          157 
##   3356.68683  -4861.10877  -1174.68113  -3236.15911   5955.89753   9746.96780 
##          158          159          160          161          162          163 
##  -3669.37405  -5015.92277   3366.17452    -42.01924   2459.60176  -6146.82382 
##          164          165          166          167          168          169 
##  -6984.43855   3919.12619  17155.08575   3384.07736   -643.63000  -2691.33284 
##          170          171          172          173          174          175 
##  -1349.24243   3345.33500   -474.56706  -8322.02699   2618.52089   4079.11216 
##          176          177          178          179          180          181 
##    377.66354   8502.04176  -9499.55761  -3720.49683 -10992.79158 -11485.98219 
##          182          183          184          185          186          187 
##    991.24162   9048.46095  -1678.39929   5679.90782   6303.17727  12901.38854 
##          188          189          190          191          192          193 
##   8165.11242  -4335.95503   2191.20445  10091.32941  -1928.73680  -2730.03904 
##          194          195          196          197          198          199 
## -10565.42162  -6642.40698    958.27888  -5508.96830 -10067.95238   5118.33060 
##          200          201          202          203          204          205 
##  -3335.98076  -1977.69084  -1068.06250   6230.84881   9612.09904    298.29770 
##          206          207          208          209          210          211 
##   2643.14507   2813.65514   5497.61050  12542.75592  -5987.38953 -11589.97362 
##          212          213          214          215          216          217 
##  -5949.68552 -10864.85948  -5343.18414   1263.57774 -13274.42927  16136.03575 
##          218          219          220          221          222          223 
##   7541.70571   1252.87180  26411.05489  12223.86689   7021.52064  13708.16404 
##          224          225          226          227          228          229 
##  -4242.38818  -2062.85555   3460.53057     43.76741   2434.15353   8695.28420 
##          230          231          232          233          234          235 
##   5519.68164  -2214.53474  -2129.49855   9129.72192 -11808.85302  -7571.80480 
##          236          237          238          239          240          241 
##  -8822.08654 -10374.64586   2812.88775   1085.00125  -8565.01362  -9250.43340 
##          242          243          244          245          246          247 
##   8840.97024  -8027.85919   2230.89897 -10560.76457  -4305.58237   1172.92263 
##          248          249          250          251          252          253 
##    749.97979 -12571.80335   3396.73517   1809.87438   3954.78585   1871.40157 
##          254          255          256          257          258          259 
##  -1427.94010  10871.22796  20599.07037   2889.06202  -4583.40677   3802.63230 
##          260          261          262          263          264          265 
##  -2005.43532   3429.43806  -5162.50700 -11197.37362  -5018.89990   -806.29433 
##          266          267          268          269          270          271 
##  -5472.43002   8499.37152  -4571.59908   3902.40992  -2400.58318   4138.96142 
##          272          273          274          275          276          277 
##    410.20091   7002.45112  -1723.49941  11715.27046  -4912.72966   1403.29092 
##          278          279          280          281          282          283 
##   -696.61165   7528.42277  -5391.67287  -3055.18933 -11578.54547  -2965.47532 
##          284          285          286          287          288          289 
##  18364.02677   7461.12053   2399.38744   -966.23792    571.62058   6064.31350 
##          290          291          292          293          294          295 
##   6539.23453 -19124.96895 -11450.71707  -8407.83787   9396.65731   2785.60327 
##          296          297          298          299          300          301 
##  -1470.72048  27113.53201   9721.48220   4539.97225   9152.26991   2477.36477 
##          302          303          304          305          306          307 
##  -1410.08697   7528.56109 -24672.49887  -3849.63685   -477.07521  -7265.39913 
##          308          309          310          311          312          313 
##  -4249.57199   2666.03130  -9463.27872  -3477.82273  -8425.39942   1345.72741 
##          314          315          316          317          318          319 
##  -3376.22684   1828.48855  -4310.53493  27223.60586  -1035.84160   2981.71725 
##          320          321          322          323          324          325 
##  10513.72922   5247.88683  32030.09726   4689.43965 -21356.10608   1439.32433 
##          326          327          328          329          330          331 
##    761.13437  -6809.21114  -2054.62604 -33577.78464    697.34470  -2487.35099 
##          332          333          334          335          336          337 
##   -271.19701  -3345.87804   3915.05190   -621.11290  -7137.08579  -3283.57894 
##          338          339          340          341          342          343 
##  -2352.93666  -7838.18432   3710.50597  -1529.52692  -1897.12877  -1153.02679 
##          344          345          346          347          348          349 
##     15.52609    315.16783  -1791.25883  -9619.46995 -13360.96972   2190.26510 
##          350          351          352          353          354          355 
##  -4458.70522  -3788.90323  -6107.49295   1632.40964   1250.84102   2605.94652 
##          356          357          358          359          360          361 
##  -3931.19205   -677.31958    510.28306   6837.35301     71.74336   -248.03552 
##          362          363          364          365          366          367 
##   2369.99522  -2974.24671  -1093.30288  -8957.22807  -4810.76925  -6383.72165 
##          368          369          370          371          372          373 
##  -5103.20291  -7394.54575   4890.87526    220.31148   6960.15018  -7827.10080 
##          374          375          376          377          378          379 
##  -2431.04269  -3552.58877  -2624.61618 -12611.63507   1785.24870 -10767.42877 
##          380          381          382          383          384          385 
##   5590.68571   9199.44072   2946.97690  -2595.37460   1411.41596   6540.11277 
##          386          387          388          389          390          391 
##  11177.94493  -6079.84894  -5623.07328   -401.32527   8317.85615   1536.70416 
##          392          393          394          395          396          397 
##  10936.84688 -10204.10366   2485.01504    414.40240    263.47997   -952.47282 
##          398          399          400          401          402          403 
##   -856.93649 -14776.66558   8294.62718  -1436.18573  -1619.93286   6741.94492 
##          404          405          406          407          408          409 
##  -8197.15287  -1527.42684  -2754.07922  -6029.15148  -3044.50631  -4091.51976 
##          410          411          412          413          414          415 
##  -8916.04014   6003.01364   1479.36789  -7546.29000  -7840.88252  14096.12406 
##          416          417          418          419          420          421 
##   3624.26477   4277.19101  -8273.66088  -4950.97857  -2789.58747   2640.29911 
##          422          423          424          425          426          427 
## -14204.14429  -2931.94250  -9233.54601   2907.46250   6850.27002   6411.71306 
##          428          429          430          431          432          433 
##  -4184.07864  -4304.86872  -4893.21867  -1946.04381  -5866.36213  -6764.47859 
##          434          435          436          437          438          439 
##  -6068.84199  -1498.27175   -957.40561  -5091.28919   2475.04384   4712.30506 
##          440          441          442          443          444          445 
##  -5211.81406  -2300.35685   1435.39607  -3991.38610   2689.93947  -6741.33570 
##          446          447          448          449          450          451 
## -12253.81236  -4615.86712   9548.09428  -2174.78025   4613.14857  -6034.44755 
##          452          453          454          455          456          457 
##  -1269.75986    235.68170   2871.54612 -12438.97620   3239.43749  -6849.88130 
##          458          459          460          461          462          463 
##   6392.43660   2852.15369   2331.67754  -4033.21012   1917.37197   -192.99045 
##          464          465          466          467          468          469 
##   1605.45081   -716.95049   3155.51112  -2847.19079   5607.43802  -7160.38535 
##          470          471          472          473          474          475 
##  -3154.46229  -2383.65780  -4834.05507   2842.02265   7629.41700  -6214.87106 
##          476          477          478          479          480          481 
##   1308.74051  -6360.42595  -3004.31792   1860.03116 -13091.84463  -9875.42081 
##          482          483          484          485          486          487 
##  -1296.14943    -78.57311  -1069.87703  -1454.32458  -9700.30889  11004.28326 
##          488          489          490          491          492          493 
##   6097.01793   7255.25525  -5630.18694   5191.87111   9097.20544   5825.84539 
##          494          495          496          497          498          499 
## -13719.76279 -10760.75882  -3598.41963  -1253.14396   -670.67249  -7773.50238 
##          500          501          502          503          504          505 
##    486.75656   4156.66397   5358.76914    488.86365    -94.32626  -7414.90276 
##          506          507          508          509          510          511 
##    417.96392  -5204.61187   1691.07143  -1447.44383  -8307.23630   -727.36574 
##          512          513          514          515          516          517 
##  -2802.87649   -712.20661   1204.31412  -9632.61443  -7875.99298  24193.93842 
##          518          519          520          521          522          523 
##   9643.58819   5663.75155  -5569.21247   2585.95118  16799.01745  11198.56894 
##          524          525          526          527          528          529 
## -24455.69713  -5275.59586  -3929.30678   4389.75580   -554.89034 -11298.87931 
##          530          531          532          533          534          535 
##   4233.28769  13734.44254  -5199.99354   4169.35477   5336.45437  -2025.93068 
##          536          537          538          539          540          541 
##  -4766.89517  -7281.35386  -2281.57745   8148.97402    -74.78497  -8342.31948 
##          542          543          544          545          546          547 
##   1644.07683   -778.23733    189.40139 -11209.36697 -11203.57668   1932.04020 
##          548          549          550          551          552          553 
##   6881.44284  -1468.49884    687.41470  -7876.08694   8432.62018    741.21012 
##          554          555          556          557          558          559 
## -12114.79836   9029.44845   8488.56336   -102.09806   4651.60181  -3792.07396 
##          560          561          562          563          564          565 
##  13904.30168  21247.22097  -6786.78711  -9968.85450   6527.82406    -39.00903 
##          566          567          568          569          570          571 
##   3195.22629  -7641.96518 -17539.92593   6495.98496   6246.60812   1694.87459 
##          572          573          574          575          576          577 
##   2887.98062   1552.95188  -2383.72919  14509.70796  -9903.19930  -6461.21412 
##          578          579          580          581          582          583 
##   8518.43585   2637.82866  -6772.02554   7308.35176  -4023.10037  -2982.07811 
##          584          585          586          587          588          589 
##  15508.23058 -14744.08209   8236.23071   -147.74456  -6426.61523   -943.64501 
##          590          591          592          593          594          595 
##     67.26463 -10836.94506   1649.82539  -7298.71434   2935.78198   8721.42670 
##          596          597          598          599          600          601 
##  -7676.83969   5699.71344   2562.15745   6673.85300  -3393.75103   5959.04171 
##          602          603          604          605          606          607 
##  -8507.01166   2077.27563   1085.81503   2951.69398   1300.45882    200.64534 
##          608          609          610          611          612          613 
##  -6005.50387   7901.46644  -1381.54166  -2764.46077  -3632.10516  -8393.61326 
##          614          615          616          617          618          619 
##  11820.52270   4759.35254  -9505.13658  11467.97851   5855.76565  -5782.11377 
##          620          621          622          623          624          625 
##  26173.67040 -13086.53940  -6989.00975   2975.18557  -4347.20714 -10763.69513 
##          626          627          628          629          630          631 
##  11159.04986 -21807.41596  -2522.05196   8571.01443  11001.90397  -1720.07525 
##          632          633          634          635          636          637 
##  33122.90993  -6842.79437   5491.09161   5164.45571  -2509.97506  -5570.44688 
##          638          639          640          641          642          643 
##  -2141.97888 -12621.72459  -2393.56115  -2030.60371  -2659.52161  -2990.79610 
##          644          645          646          647          648          649 
##   1688.95485   4297.61960  16817.57943  18357.57146    639.20577   4550.63822 
##          650          651          652          653          654          655 
##  10368.09210  19886.75193    441.09901 -28349.98281  -1534.88435  -2472.93649 
##          656          657          658          659          660          661 
##   1700.05225  -3358.79555 -10774.36244   1543.02202   4101.22775  -1141.58616 
##          662          663          664          665          666          667 
##  12901.41412   1198.80658   1652.54324 -11852.43102   1249.23926   1055.47973 
##          668          669          670          671          672          673 
##  -5298.90428  -7528.91499   1965.90604  -3817.92096   2574.86473  -3485.02105 
##          674          675          676          677          678          679 
##  -9436.29787  -8388.55475  -3049.04440    100.30883   2767.97962    623.22636 
##          680          681          682          683          684          685 
##  -3921.63736  -1899.01862  -1408.89232  -8334.11670   4569.30911  -2335.79471 
##          686          687          688          689          690          691 
##  -1490.63688    494.36843  10756.17825   9729.01620  10485.30496  -9816.58234 
##          692          693          694          695          696          697 
##  -3682.75232  -3259.12393   5758.73396 -10507.67844  -8012.76769  -8699.11921 
##          698          699          700          701          702          703 
##  -6350.16211  -4809.06037   3014.62590  -4480.07258  -1973.54729   4145.32963 
##          704          705          706          707          708          709 
##  31019.15724   9407.73574  23335.34597   1573.70166   8225.53312  22829.67694 
##          710          711          712          713          714          715 
##   6476.48590 -18278.36903   4758.67962  -5503.79325   -158.20616    422.96184 
##          716          717          718          719          720          721 
## -17323.03470  -5319.33085   3280.00204  -3068.55408 -13033.21942   4225.91495 
##          722          723          724          725          726          727 
##  -5610.63381    688.26532  -3989.24749 -12501.76019   1313.83681  -1921.37465 
##          728          729          730          731          732          733 
##  -9832.07214  17214.83400   1719.16680  -2779.12781   5659.03716  -8687.23402 
##          734          735          736          737          738          739 
##   -779.23831   8082.47822 -15408.10509  -5966.25717   7353.14856  -4840.52985 
##          740          741          742          743          744          745 
##    105.03403   1771.67749  -2012.24598  -5224.38752   6356.61866  -6331.70082 
##          746          747          748          749          750          751 
##  22642.50457   7769.64662  -2004.32676  -7344.98519  23360.43198  -4346.74707 
##          752          753          754          755          756          757 
##   1341.99292 -14475.77436  56055.88286  26874.97213  15056.98950 -10678.77428 
##          758          759          760          761          762          763 
##  10575.18467   7285.44128   5782.15330 -46415.20684 -16203.90584    930.69154 
##          764          765          766          767          768          769 
##  -2552.99129  -3493.66735 122807.63988  19313.58670  43727.10207  22604.95322 
##          770          771          772          773          774          775 
##  12099.13861  15869.21567  25749.53773 -98783.23509  -6772.40093 -35850.97074 
##          776          777          778          779          780          781 
##   1704.09017  -1260.75540   3364.11847  -7445.03480  -1477.53774  -1977.67850 
##          782          783          784          785          786          787 
##   3391.98528  -7185.61982  -2269.16311   3887.07649   2235.38636  -2787.36761 
##          788          789          790          791          792          793 
##  -4075.99929   1709.57371   2825.58520    -77.43441  -6728.86901  -5809.98389 
##          794          795          796          797          798          799 
##  -1176.38227  -1291.91087  -7845.87453  -2384.84997  -3299.16813  -2696.95226 
##          800          801          802          803          804          805 
##  10692.80477   2214.63431   7054.41662   2901.20276  -5469.30110   8164.56277 
##          806          807          808          809          810          811 
##   9854.99705 -10618.29660  -7416.47384  -7527.59098   2981.28070   4171.25886 
##          812          813          814          815          816          817 
##  -2290.74438 -14186.63246  -4136.66089   6232.80382   8213.22968  -9693.73945 
##          818          819          820          821          822          823 
##  -7773.41083  -9361.50870   9726.36582  -1246.56072  -4395.31640  -8471.38801 
##          824          825          826          827          828          829 
##   7848.16395   7890.70630   4954.93597  -3121.45377   -732.04945   2871.57754 
##          830          831          832          833          834          835 
##   4648.84862   1605.72637  -6712.55127   2068.03178   -418.66411   2732.67373 
##          836          837          838          839          840          841 
##   4120.44664  -1155.87689  -9354.84814   5654.07368  -4881.90642  -7599.28519 
##          842          843          844          845          846          847 
##   2999.25952 -13065.24893  -2843.08449  11604.21755   1290.30246    614.78598 
##          848          849          850 
##   -681.61564   4492.45933  12666.85928 
## 
## $fitted.values
##         2         3         4         5         6         7         8         9 
##  17251.84  20099.24  24353.57  24071.51  26425.07  23757.50  24473.88  19705.61 
##        10        11        12        13        14        15        16        17 
##  19442.15  16784.69  17562.22  14291.36  14342.53  15007.23  16703.95  15023.83 
##        18        19        20        21        22        23        24        25 
##  16058.58  15432.07  22515.08  21599.02  21078.83  22968.99  22294.92  22947.52 
##        26        27        28        29        30        31        32        33 
##  24793.63  18721.53  20447.82  28285.77  28343.43  28016.32  25645.54  27048.09 
##        34        35        36        37        38        39        40        41 
##  30891.44  31239.89  32649.18  30159.58  34137.44  37345.78  34401.65  31212.28 
##        42        43        44        45        46        47        48        49 
##  30059.96  20638.74  28158.07  30594.75  31684.04  38522.68  38017.22  42679.78 
##        50        51        52        53        54        55        56        57 
##  46916.39  39613.72  34181.74  29204.05  22347.94  28638.44  25218.81  21516.12 
##        58        59        60        61        62        63        64        65 
##  25924.82  27184.47  27481.51  27893.36  23763.96  40356.55  42200.66  37446.25 
##        66        67        68        69        70        71        72        73 
##  41653.52  46569.56  57238.57  55252.18  40494.21  37999.88  41018.02  35317.71 
##        74        75        76        77        78        79        80        81 
##  30769.60  21472.31  24674.04  20599.43  22685.66  17580.79  19596.38  18822.84 
##        82        83        84        85        86        87        88        89 
##  17850.49  15919.27  17197.14  20805.48  25223.55  26213.50  26238.46  26855.24 
##        90        91        92        93        94        95        96        97 
##  30980.64  29811.10  30810.53  28874.82  28074.58  28442.75  28849.36  22426.31 
##        98        99       100       101       102       103       104       105 
##  25441.58  18471.52  17327.56  15368.14  15667.92  16345.35  20818.34  19901.77 
##       106       107       108       109       110       111       112       113 
##  23413.14  23203.09  24879.34  27756.32  25254.25  21697.46  21972.90  24619.37 
##       114       115       116       117       118       119       120       121 
##  35484.70  33677.41  35515.03  38513.57  40469.63  38157.88  32978.51  29319.62 
##       122       123       124       125       126       127       128       129 
##  31402.60  29682.43  30864.07  38464.40  38106.83  37168.31  34031.60  35812.93 
##       130       131       132       133       134       135       136       137 
##  41211.21  40651.47  31852.52  33117.41  36307.41  32717.79  31104.95  30189.88 
##       138       139       140       141       142       143       144       145 
##  26746.66  28161.19  27930.80  25616.93  27641.51  26266.16  19867.83  22898.57 
##       146       147       148       149       150       151       152       153 
##  20725.11  23702.44  24242.46  25832.97  26015.06  27669.34  28970.17  32002.54 
##       154       155       156       157       158       159       160       161 
##  27472.40  26735.30  24290.39  30184.89  41689.80  40019.92  37384.68  42405.30 
##       162       163       164       165       166       167       168       169 
##  43813.97  47230.11  42695.72  38002.59  43428.20  59731.49  61943.77  60357.76 
##       170       171       172       173       174       175       176       177 
##  57183.24  55582.38  58285.14  57309.17  49600.76  52424.46  56167.34  56203.53 
##       178       179       180       181       182       183       184       185 
##  63332.84  53834.50  50585.22  41393.27  32932.04  36440.54  46544.69  46000.66 
##       186       187       188       189       190       191       192       193 
##  51953.82  57699.18  68482.89  73766.10  67460.37  67653.81  74724.59  70400.75 
##       194       195       196       197       198       199       200       201 
##  65923.28  55166.41  49196.15  50620.54  46214.95  38383.24  44808.41  43035.69 
##       202       203       204       205       206       207       208       209 
##  42673.63  43152.01  49946.47  58836.27  58465.85  60190.77  61846.68  65638.10 
##       210       211       212       213       214       215       216       217 
##  75105.25  67187.55  55375.83  49984.29  40980.04  37937.57  41051.43  31070.96 
##       218       219       220       221       222       223       224       225 
##  48045.58  55366.84  56268.80  79035.70  86531.19  88534.55  96126.39  87076.71 
##       226       227       228       229       230       231       232       233 
##  81074.76  80656.66  77306.42  76467.86  81205.18  82569.53  77004.64  72217.28 
##       234       235       236       237       238       239       240       241 
##  77871.28  64518.23  56554.23  48504.36  40115.40  44307.57  46460.44  39910.72 
##       242       243       244       245       246       247       248       249 
##  33589.89  43873.00  38119.53  42055.48  34318.87  33024.65  36680.16  39504.23 
##       250       251       252       253       254       255       256       257 
##  30333.12  36271.55  40073.21  45268.31  47986.80  47479.34  57780.93  75279.22 
##       258       259       260       261       262       263       264       265 
##  75094.26  68404.51  69886.44  66106.99  67553.22  61310.52  50584.47  46611.58 
##       266       267       268       269       270       271       272       273 
##  46821.00  42927.49  51732.17  48005.02  52152.01  50268.47  54336.08  54632.12 
##       274       275       276       277       278       279       280       281 
##  60649.93  58284.02  67957.59  61881.99  62092.04  60441.01  66184.24  59914.33 
##       282       283       284       285       286       287       288       289 
##  56477.97  46029.62  44426.26  61659.59  67190.04  67599.52  65016.95  64104.26 
##       290       291       292       293       294       295       296       297 
##  68105.48  72015.97  53011.29  43112.70  37123.34  47445.40  50687.43  49801.33 
##       298       299       300       301       302       303       304       305 
##  73999.23  79945.03  80612.73  85225.49  83423.94  78453.87  81920.93  56818.07 
##       306       307       308       309       310       311       312       313 
##  53078.93  52758.68  46548.43  43757.68  47361.28  39912.97  38634.97  33196.13 
##       314       315       316       317       318       319       320       321 
##  36980.94  36162.23  39993.96  37978.25  63766.41  61607.43  63231.13  71229.83 
##       322       323       324       325       326       327       328       329 
##  73617.33  99100.85  97478.39  73306.82  72104.58  70461.78  62412.91  59534.93 
##       330       331       332       333       334       335       336       337 
##  29481.08  33168.92  33608.48  35928.59  35269.38  41036.83  42112.51  37359.72 
##       338       339       340       341       342       343       344       345 
##  36574.08  36700.76  32019.35  38018.81  38682.27  38940.74  39816.62  41602.69 
##       346       347       348       349       350       351       352       353 
##  43424.83  43176.47  36120.54  26687.59  32032.71  30893.62  30483.64  28099.88 
##       354       355       356       357       358       359       360       361 
##  32779.16  36533.77  40997.76  39186.61  40447.00  42585.65  49981.54  50532.18 
##       362       363       364       365       366       367       368       369 
##  50733.86  53197.25  50680.45  50124.94  42769.48  39966.01  36142.63  33921.12 
##       370       371       372       373       374       375       376       377 
##  29978.55  37267.12  39554.28  47440.53  41411.61  40858.73  39395.90  38928.64 
##       378       379       380       381       382       383       384       385 
##  29795.47  34394.00  27445.03  35665.13  45999.17  49564.95  47838.16  49830.03 
##       386       387       388       389       390       391       392       393 
##  56050.77  65537.13  58747.79  53215.47  52944.14  60324.44  60847.87  69517.39 
##       394       395       396       397       398       399       400       401 
##  58621.98  60189.03  59749.09  59232.90  57719.65  56481.09  43238.37  51824.90 
##       402       403       404       405       406       407       408       409 
##  50825.22  49791.34  56193.30  48735.00  48046.08  46372.58  42049.36  40879.95 
##       410       411       412       413       414       415       416       417 
##  38943.61  33037.13  40910.77  43837.43  38509.17  33596.88  48470.16  52315.38 
##       418       419       420       421       422       423       424       425 
##  56245.09  48713.41  45036.30  43712.13  47299.00  35716.80  35445.97  29704.11 
##       426       427       428       429       430       431       432       433 
##  35294.59  43623.14  50516.08  47281.15  44349.50  41274.33  41162.50  37639.91 
##       434       435       436       437       438       439       440       441 
##  33777.84  31011.56  32587.83  34437.43  32441.81  37308.55  43514.81  40266.79 
##       442       443       444       445       446       447       448       449 
##  39972.75  42979.53  40865.35  44855.34  40101.67  31132.87  29970.19  41328.49 
##       450       451       452       453       454       455       456       457 
##  41009.99  46661.88  42297.47  42647.18  44267.88  47986.55  37859.56  42709.45 
##       458       459       460       461       462       463       464       465 
##  38132.13  45702.13  49222.61  51843.50  48572.63  50913.70  51115.26  52862.52 
##       466       467       468       469       470       471       472       473 
##  52360.06  55304.19  52632.13  57683.96  50943.03  48553.66  47139.63  43763.55 
##       474       475       476       477       478       479       480       481 
##  47520.15  54984.44  49410.69  51114.14  45902.32  44281.11  47114.42  36527.28 
##       482       483       484       485       486       487       488       489 
##  30088.01  31957.57  34654.59  36144.75  37110.74  30750.72  43282.55  49943.60 
##       490       491       492       493       494       495       496       497 
##  56774.76  51485.56  56319.22  63953.87  67765.76  54020.33  44596.99  42621.72 
##       498       499       500       501       502       503       504       505 
##  42944.96  43736.22  38222.24  40621.48  45923.66  51605.99  52315.75  52426.33 
##       506       507       508       509       510       511       512       513 
##  46127.46  47467.61  43726.36  46482.16  46147.81  39862.79  40994.02  40169.06 
##       514       515       516       517       518       519       520       521 
##  41274.83  43915.19  36754.42  32033.20  55925.84  64087.53  67740.93  61119.19 
##       522       523       524       525       526       527       528       529 
##  62458.84  76046.15  83023.70  57970.88  52840.31  49534.24  53913.75  53420.02 
##       530       531       532       533       534       535       536       537 
##  43602.43  48594.84  61256.85  55777.07  59175.12  63163.36  60215.61  55245.78 
##       538       539       540       541       542       543       544       545 
##  48707.29  47363.03  55301.07  55051.46  47610.64  49834.52  49661.17  50355.08 
##       546       547       548       549       550       551       552       553 
##  41003.01  32837.82  37180.13  45297.64  45094.59  46800.66  40809.81  49823.79 
##       554       555       556       557       558       559       560       561 
##  50979.23  40757.27  50299.29  58162.96  57527.83  61125.93  56892.70  68654.49 
##       562       563       564       565       566       567       568       569 
##  85344.93  75434.85  63997.18  68416.87  66541.06  67727.82  59296.93  43284.30 
##       570       571       572       573       574       575       576       577 
##  50293.68  56199.41  57382.31  59458.05  60105.16  57231.29  69479.20  58851.50 
##       578       579       580       581       582       583       584       585 
##  52573.85  60176.17  61680.31  54773.65  61040.81  56616.51  53660.77  67232.22 
##       586       587       588       589       590       591       592       593 
##  52659.34  60004.32  59096.62  52818.22  52123.31  52399.37  43114.32  45911.43 
##       594       595       596       597       598       599       600       601 
##  40537.36  44783.57  53547.70  46878.29  52737.84  55115.86  60785.47  56943.24 
##       602       603       604       605       606       607       608       609 
##  61757.44  53325.30  55205.47  55981.88  58290.26  58864.35  58405.08  52581.96 
##       610       611       612       613       614       615       616       617 
##  59644.26  57704.18  54801.11  51506.90  44469.19  55980.50  59868.28  50802.88 
##       618       619       620       621       622       623       624       625 
##  61205.81  65391.11  58880.33  81109.83  66231.30  58559.96  60563.06  55915.98 
##       626       627       628       629       630       631       632       633 
##  46250.52  56958.84  37513.48  37373.70  46942.81  57426.36  55470.80  84202.22 
##       634       635       636       637       638       639       640       641 
##  74387.62  76588.54  78225.98  72951.88  65670.55  62304.58  50208.56  48576.75 
##       642       643       644       645       646       647       648       649 
##  47468.24  45950.37  44334.90  47011.95  51629.71  66601.71  81027.08  78150.22 
##       650       651       652       653       654       655       656       657 
##  79054.05  84925.96  98371.62  93129.84  63397.74  60849.37  57803.52  58788.22 
##       658       659       660       661       662       663       664       665 
##  55228.93  45640.98  48025.49  52343.59  51535.73  63098.34  62976.03  63265.57 
##       666       667       668       669       670       671       672       673 
##  51720.19  53079.81  54098.33  49436.77  43416.09  46451.21  44049.85  47536.88 
##       674       675       676       677       678       679       680       681 
##  45289.16  38126.27  32783.90  32781.41  35530.59  40262.92  42523.49  40527.88 
##       682       683       684       685       686       687       688       689 
##  40551.46  41000.26  35342.26  41672.08  41169.49  41468.77  43464.39  54172.84 
##       690       691       692       693       694       695       696       697 
##  62630.70  70680.44  59976.61  55984.12  52866.27  58020.68  48312.91  42011.55 
##       698       699       700       701       702       703       704       705 
##  35906.88  32625.77  31105.66  36612.64  34876.12  35548.81  41482.13  70143.41 
##       706       707       708       709       710       711       712       713 
##  76302.37  93850.58  90169.61  92765.04 107791.09 106631.65  83992.18  84339.51 
##       714       715       716       717       718       719       720       721 
##  75677.35  72779.90  70756.32  53485.05  48883.14  52375.41  49880.08  38994.66 
##       722       723       724       725       726       727       728       729 
##  44562.92  40834.02  43079.25  40954.33  31661.16  35612.09  36237.36  29872.59 
##       730       731       732       733       734       735       736       737 
##  47941.12  50188.84  48222.68  53876.81  46283.10  46557.66  54539.39  40990.40 
##       738       739       740       741       742       743       744       745 
##  37402.28  45903.82  42678.25  44180.89  46949.67  46062.82  42481.81  49470.84 
##       746       747       748       749       750       751       752       753 
##  44491.78  65454.64  70775.04  66884.27  58819.43  78598.89  71673.01  70592.20 
##       754       755       756       757       758       759       760       761 
##  55829.12 104550.17 121621.01 126210.06 107735.67 110163.99 109411.42 107440.64 
##       762       763       764       765       766       767       768       769 
##  60117.76  45168.59  47077.85  45702.38  43678.93 152251.70 156688.61 181893.19 
##       770       771       772       773       774       775       776       777 
## 185459.72 179397.36 177394.75 184276.95  81493.97  72083.11  38457.62  41890.61 
##       778       779       780       781       782       783       784       785 
##  42299.60  46697.32  41096.11  41416.11  41258.73  45812.33  40549.59  40247.07 
##       786       787       788       789       790       791       792       793 
##  45361.04  48385.80  46640.29  43989.57  46728.27  50095.86  50501.73  45045.41 
##       794       795       796       797       798       799       800       801 
##  41081.38  41666.34  42076.45  36708.99  36790.74  36063.38  35954.05  47556.22 
##       802       803       804       805       806       807       808       809 
##  50285.44  56897.94  59046.44  53610.72  60772.86  68506.73  57377.19  50451.31 
##       810       811       812       813       814       815       816       817 
##  44303.58  48113.60  52481.74  50652.49  38661.80  36966.34  44544.20  52894.60 
##       818       819       820       821       822       823       824       825 
##  44545.70  38929.51  32635.63  43812.85  43991.32  41396.39  35568.41  44734.15 
##       826       827       828       829       830       831       832       833 
##  52778.78  57242.03  54085.48  53415.28  55978.01  59769.56  60423.41  53727.54 
##       834       835       836       837       838       839       840       841 
##  55548.81  54967.47  57212.70  60386.59  58549.85  49782.35  55235.05  50794.14 
##       842       843       844       845       846       847       848       849 
##  44540.45  48336.25  37617.94  37184.50  49427.41  51112.64  51994.76  51632.83 
##       850 
##  55836.86 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8093
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original      bias    std. error
## t1*    5.262042   0.7879612    4.009273
## t2* 2715.568547 164.8153807  887.557418
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.187809       5.253548   13.73319
## 2    lag_depvar 1667.507919    2753.649754 4539.71669

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
    dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03")))

# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
  Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
  Data = as.numeric(mstl_data_autplt[, "Data"]),
  Trend = as.numeric(mstl_data_autplt[, "Trend"]),
  Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)

# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
  pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")

# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
  facet_wrap(~ Component, scales = "free_y", ncol = 1) +
  theme(strip.text = element_text(size = 12),
        axis.text.x = element_text(angle = 90, hjust = 1))

library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
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#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_25<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2025",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2020",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_25 %>% 
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))
Item 2025 2024 2023 2022 2021 2020
Agua 8.205857 6.993667 5.195333 5.410333 5.849167 9.93775
Comida 192.101143 326.890000 366.009167 312.386750 317.896583 392.93367
Comunicaciones 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000
Electricidad 54.522571 83.582750 38.104750 47.072333 29.523000 20.60458
Enceres 1.884286 23.989000 18.259750 24.219750 14.801167 39.01200
Farmacia 0.000000 0.000000 10.704083 2.835000 13.996083 14.03675
Gas/Bencina 26.407143 44.292667 42.636000 45.575000 13.583667 17.25833
Diosi 14.927000 33.319583 55.804250 31.180667 52.687833 37.12133
donaciones/regalos 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000
Electrodomésticos/ Mantención casa 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000
VTR 15.711429 18.326667 12.829167 25.156667 19.086917 19.11375
Netflix 0.000000 1.391417 8.713833 7.151583 7.028750 8.24725
Otros 0.000000 76.164000 5.481667 5.000000 0.000000 0.00000
Total 313.759429 614.949750 563.738000 505.988083 474.453167 558.26542
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")

tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  54 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2777, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)
## Warning in bats(as.numeric(y), use.box.cox = use.box.cox, use.trend =
## use.trend, : optim() did not converge.
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)
# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))
## API key has been set for the current session.
try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))
## API key has been set for the current session.
# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
    dplyr::rename(ds = date3, y = value) %>%
    dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
    dplyr::mutate(unique_id = "serie_1")%>%
    dplyr::select(unique_id, ds, y)

# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
  uf_timegpt,
  h = 298,               # 298 días a pronosticar
  freq = "D",            # Frecuencia diaria
  add_history = TRUE,     # Incluir datos históricos en el output
  level = c(80,95),
  model=  "timegpt-1-long-horizon", 
  clean_ex_first = TRUE
)
## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>% 
    mutate(ds = as.Date(ds))

timegpt_fcst <- timegpt_fcst %>% 
    mutate(ds = as.Date(ds))

# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
    uf_timegpt %>% mutate(type = "Histórico"),
    timegpt_fcst %>% mutate(type = "Pronóstico")
)

# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
    # Intervalo de confianza del 95%
    geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`), 
                fill = "#4B9CD3", alpha = 0.2) +
    # Intervalo de confianza del 80%
    geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`), 
                fill = "#4B9CD3", alpha = 0.3) +
    # Línea histórica
    geom_line(data = filter(full_data, type == "Histórico"), 
              aes(color = "Histórico"), size = 1) +
    # Línea de pronóstico
    geom_line(data = filter(full_data, type == "Pronóstico"), 
              aes(color = "Pronóstico"), size = 1) +
    # Línea vertical separadora
    geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds), 
               linetype = "dashed", color = "red", size = 0.8) +
    # Configuración del eje x
    scale_x_date(
        date_breaks = "3 months",  # Reduce la frecuencia de las etiquetas
        date_labels = "%b %Y",  # Formato de etiquetas (mes y año)
    ) +
    # Configuración del eje y
    scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
    # Configuración de colores
    scale_color_manual(
        name = "Leyenda",
        values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
    ) +
    # Títulos y subtítulos
    labs(
        title = "Pronóstico de Serie Temporal con TimeGPT",
        subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
        x = "Fecha",
        y = "Valor",
        color = "Leyenda"
    ) +
    # Tema y estilos
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.position = "bottom",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()
    )
## Warning: Removed 2777 rows containing missing values or values outside the scale range
## (`geom_line()`).

library(prophet)
## Warning: package 'prophet' was built under R version 4.4.3
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 4.4.3
## Loading required package: rlang
## Warning: package 'rlang' was built under R version 4.4.3
## 
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
## 
##     %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
##     flatten_raw, invoke, splice
## The following object is masked from 'package:sparklyr':
## 
##     invoke
## The following object is masked from 'package:data.table':
## 
##     :=
  model <- prophet(
  cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
  # Trend flexibility
  growth = "linear",
  changepoint.prior.scale = 0.05,  # Reduced for smoother trend
  n.changepoints = 50,  # Increased from default 25
  
  # Seasonality
  yearly.seasonality = TRUE,
  weekly.seasonality = TRUE,
  daily.seasonality = FALSE,  # Disabled for daily data
  seasonality.mode = "additive",
  seasonality.prior.scale = 15,  # Increased to capture stronger seasonality
  
  # Holidays (if applicable)
  # holidays = generated_holidays  # Create with add_country_holidays()
  
  # Uncertainty intervals
  interval.width = 0.95,
  uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)
## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistent
forecast$ds <- as.Date(forecast$ds)

ggplot(forecast, aes(x = ds, y = yhat)) +
  geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper), 
              fill = "#9ecae1", alpha = 0.4) +
  geom_line(color = "#08519c", linewidth = 0.8) +
  geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
  scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
  scale_y_continuous(labels = scales::comma) +
  labs(title = "Valores predichos (95%IC)",
      # subtitle = "March 10, 2025 - May 7, 2025",
       x = "Fecha",
       y = "Valor",
      # caption = "Source: Prophet Forecast Model"
      ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(color = "gray50"),
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    plot.caption = element_text(color = "gray30")
  )

La proyección de la UF a 298 días más 2025-08-09 00:04:58 sería de: 26.443 pesos// Percentil 95% más alto proyectado: 34.867,93

Según TimeGPT: La proyección de la UF a 298 días más 2026-06-03 sería de: 39.627,14 pesos// Percentil 80% más alto proyectado: 39.944,44 pesos// Percentil 95% más alto proyectado: 40.252,42

Según prophet: La proyección de la UF a 298 días más 2026-06-03 sería de: 42.418 pesos// Percentil 95% más alto proyectado: 51.833

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 26067.78 26331.96
Lo.80 26196.95 26493.62
Point.Forecast 26442.72 26799.01
Hi.80 31244.83 32070.87
Hi.95 34130.49 34861.62


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1       mean
##       0.4788  1028.6175
## s.e.  0.1010    41.4439
## 
## sigma^2 = 38077:  log likelihood = -521.14
## AIC=1048.28   AICc=1048.61   BIC=1055.35
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept    xreg
##       0.4669   735.1797  9.0494
## s.e.  0.1009   315.9266  9.6535
## 
## sigma^2 = 38168:  log likelihood = -520.71
## AIC=1049.42   AICc=1049.97   BIC=1058.85
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 611.0632 592.9478 566.8658
Lo.80 760.9455 743.7341 659.1232
Point.Forecast 1044.0800 1028.5762 876.3411
Hi.80 1327.2145 1313.4183 1165.1444
Hi.95 1477.0969 1464.2046 1354.7715


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()
Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)

Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))

Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")

Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))

Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
  mutate(value = Tami + Andrés) %>%
  rename(ds = mes_ano, y = value) %>%
  mutate(#ds= format(ds, "%Y-%m"),
         unique_id = "1") %>% #it is only one series
  select(unique_id, ds, y)

#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
  dplyr::rename(ds = date3, y = value) %>%
  dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
  dplyr::mutate(unique_id = "serie_1")%>%
  dplyr::select(unique_id, ds, y) %>%
  mutate(ds = ymd(ds)) %>%  # Convert 'ds' to Date
  mutate(month = month(ds), year = year(ds)) %>%  # Extract month and year
  group_by(month, year) %>%  # Group by month and year
  summarise(average_y = mean(y))%>%  # Calculate average y
  mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
  ungroup()%>%
  select(ds, uf=average_y)

Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |> 
  dplyr::left_join(uf_timegpt_my, by=c("ds"="ds")) 

#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
  Gastos_casa_mensual_2022_timegpt_ex,
  h = 12,
  freq = "M",  # Monthly frequency
  add_history = TRUE,
  level = c(80, 95),
  model = "timegpt-1",#"timegpt-1-long-horizon",
  clean_ex_first = TRUE
)

# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

# Combine historical and forecast data
full_data_gastos <- bind_rows(
  Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
  gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)

full_data_gastos |> 
  dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |> 
# Visualize results
ggplot(aes(x = ds, y = y)) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
              fill = "#4B9CD3", alpha = 0.2) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
              fill = "#4B9CD3", alpha = 0.3) +
  geom_line(aes(color = type), linewidth = 1.5) +
  geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
             linetype = "dashed", color = "red", linewidth = 0.8) +
  scale_x_date(
    date_breaks = "3 months",
    date_labels = "%b %Y"
  ) +
  scale_y_continuous(
    name = "Gastos Totales",
    labels = scales::comma,
    breaks = pretty(full_data_gastos$y, n = 10),
    expand = expansion(mult = c(0.05, 0.05))
  ) +
  scale_color_manual(
    name = "Leyenda",
    values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
  ) +
  labs(
    title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
    subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
    x = "Fecha",
    y = "Gastos Totales",
    color = "Leyenda"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.title.x = element_text(size = 10),
    axis.title.y = element_text(size = 10),
    legend.position = "bottom",
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()
  )


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## system code page: 65001
## 
## time zone: UTC
## tzcode source: internal
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] prophet_1.0         rlang_1.1.6         Rcpp_1.1.0         
##  [4] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [7] Boom_0.9.15         scales_1.4.0        ggiraph_0.8.13     
## [10] tidytext_0.4.3      DT_0.33             janitor_2.2.1      
## [13] autoplotly_0.1.4    rvest_1.0.4         plotly_4.11.0      
## [16] xts_0.14.1          forecast_8.24.0     wordcloud_2.6      
## [19] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-16          
## [22] NLP_0.3-2           tsibble_1.1.6       lubridate_1.9.4    
## [25] forcats_1.0.0       dplyr_1.1.4         purrr_1.1.0        
## [28] tidyr_1.3.1         tibble_3.3.0        tidyverse_2.0.0    
## [31] gsynth_1.2.1        sjPlot_2.9.0        lattice_0.22-6     
## [34] GGally_2.3.0        ggplot2_3.5.2       gridExtra_2.3      
## [37] plotrix_3.8-4       sparklyr_1.9.1      httr_1.4.7         
## [40] readxl_1.4.5        zoo_1.8-14          stringr_1.5.1      
## [43] stringi_1.8.7       DataExplorer_0.8.4  data.table_1.17.8  
## [46] reshape2_1.4.4      fUnitRoots_4040.81  plyr_1.8.9         
## [49] readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] bitops_1.0-9        cellranger_1.1.0    httr2_1.2.1        
##   [4] lifecycle_1.0.4     StanHeaders_2.32.10 doParallel_1.0.17  
##   [7] globals_0.18.0      vroom_1.6.5         MASS_7.3-60.2      
##  [10] crosstalk_1.2.1     magrittr_2.0.3      sass_0.4.10        
##  [13] rmarkdown_2.29      jquerylib_0.1.4     yaml_2.3.10        
##  [16] fracdiff_1.5-3      doRNG_1.8.6.2       askpass_1.2.1      
##  [19] pkgbuild_1.4.8      DBI_1.2.3           abind_1.4-8        
##  [22] quadprog_1.5-8      nnet_7.3-19         rappdirs_0.3.3     
##  [25] sandwich_3.1-1      inline_0.3.21       data.tree_1.1.0    
##  [28] tokenizers_0.3.0    listenv_0.9.1       anytime_0.3.12     
##  [31] spatial_7.3-17      parallelly_1.45.1   codetools_0.2-20   
##  [34] xml2_1.3.8          tidyselect_1.2.1    farver_2.1.2       
##  [37] urca_1.3-4          its.analysis_1.6.0  matrixStats_1.5.0  
##  [40] stats4_4.4.0        jsonlite_2.0.0      ellipsis_0.3.2     
##  [43] Formula_1.2-5       iterators_1.0.14    systemfonts_1.2.3  
##  [46] foreach_1.5.2       tools_4.4.0         glue_1.8.0         
##  [49] xfun_0.52           TTR_0.24.4          ggfortify_0.4.19   
##  [52] loo_2.8.0           withr_3.0.2         timeSeries_4041.111
##  [55] fastmap_1.2.0       boot_1.3-30         openssl_2.3.3      
##  [58] caTools_1.18.3      digest_0.6.37       timechange_0.3.0   
##  [61] R6_2.6.1            lfe_3.1.1           colorspace_2.1-1   
##  [64] networkD3_0.4.1     gtools_3.9.5        generics_0.1.4     
##  [67] htmlwidgets_1.6.4   ggstats_0.10.0      pkgconfig_2.0.3    
##  [70] gtable_0.3.6        timeDate_4041.110   lmtest_0.9-40      
##  [73] S7_0.2.0            selectr_0.4-2       janeaustenr_1.0.0  
##  [76] htmltools_0.5.8.1   carData_3.0-5       tseries_0.10-58    
##  [79] snakecase_0.11.1    knitr_1.50          rstudioapi_0.17.1  
##  [82] tzdb_0.5.0          uuid_1.2-1          nlme_3.1-164       
##  [85] curl_6.4.0          cachem_1.1.0        KernSmooth_2.23-22 
##  [88] parallel_4.4.0      fBasics_4041.97     pillar_1.11.0      
##  [91] vctrs_0.6.5         gplots_3.2.0        slam_0.1-55        
##  [94] car_3.1-3           dbplyr_2.5.0        xtable_1.8-4       
##  [97] evaluate_1.0.4      mvtnorm_1.3-3       cli_3.6.5          
## [100] compiler_4.4.0      crayon_1.5.3        rngtools_1.5.2     
## [103] future.apply_1.20.0 labeling_0.4.3      rstan_2.32.7       
## [106] QuickJSR_1.8.0      viridisLite_0.4.2   assertthat_0.2.1   
## [109] lazyeval_0.2.2      Matrix_1.7-0        hms_1.1.3          
## [112] bit64_4.6.0-1       future_1.67.0       nixtlar_0.6.2      
## [115] extraDistr_1.10.0   igraph_2.1.4        RcppParallel_5.1.10
## [118] bslib_0.9.0         quantmod_0.4.28     bit_4.6.0
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))